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Technological unemployment in industrial countries

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Abstract

Using annual data on 21 industrial countries from the period 1985 to 2009 and a large number of controls, this paper empirically analyzes the impact of technological change on unemployment. As proxy for technological change, it uses the ratio of triadic patent families to population. According to the regression results, an increase in technological change substantially increases unemployment over 3 years. There is no long-term effect, though. The results are robust to both endogeneity and numerous variations in specifications. They support theoretical contributions according to which faster technological progress may increase unemployment, at least during a transition period.

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Notes

  1. The summary of the early literature in this and the next paragraph is mainly based on Vivarelli (1995, 2007) and Spiezia and Vivarelli (2002). See also Hagemann (1995).

  2. On the strength and weaknesses of patent indicators, and the differential use of patents across firms and industries, see Levin et al. (1987), Archibugi (1992), Arundel and Kabla (1998), Cohen et al. (2000), Jaffe and Trajtenberg (2002), Dernis and Khan (2004), and Kleinknecht and Reinders (2012).

  3. For a survey of the relevant literature, see Aidt and Tzannatos (2008).

  4. Acemoglu and Shimer (2000) and Chetty (2008), among others, argue that generous unemployment benefits allow workers to find better matches. They do not, however, argue that this leads to lower unemployment.

  5. For example, if a country managed to increase its number of patents over the past 4 years, it is likely to achieve a higher level of patents in the current year. This basic theoretical reasoning is corroborated by the results from our first-stage regressions: in each case, the coefficients on the lagged and differenced ‘patents’ variables have a positive algebraic sign and are statistically significant, mostly at the 1 % level (results not reported here).

  6. This insight comes from, inter alia, research into GMM estimation (Arellano and Bover 1995; Blundell and Bond 1998). The latter is not an option in our case because it requires the number of cross sections to be much larger than the number of time periods as otherwise the estimates can be severely biased and imprecise (Roodman 2009a, b). Thus we follow Roodman’s (2009b) advice to use a fixed effects estimator instead.

  7. In these regressions, the lags on the excluded instruments are increased accordingly.

  8. Note, however, that we do not test any of these models systematically by taking all their assumptions into account.

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    Horst Feldmann

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Feldmann, H. Technological unemployment in industrial countries. J Evol Econ 23, 1099–1126 (2013). https://doi.org/10.1007/s00191-013-0308-6

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